{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-title"
    ]
   },
   "source": [
    "# Multiclass Support Vector Machine exercise\n",
    "\n",
    "*Complete and hand in this completed worksheet (including its outputs and any supporting code outside of the worksheet) with your assignment submission. For more details see the [assignments page](http://vision.stanford.edu/teaching/cs231n/assignments.html) on the course website.*\n",
    "\n",
    "In this exercise you will:\n",
    "    \n",
    "- implement a fully-vectorized **loss function** for the SVM\n",
    "- implement the fully-vectorized expression for its **analytic gradient**\n",
    "- **check your implementation** using numerical gradient\n",
    "- use a validation set to **tune the learning rate and regularization** strength\n",
    "- **optimize** the loss function with **SGD**\n",
    "- **visualize** the final learned weights\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [],
   "source": [
    "# Run some setup code for this notebook.\n",
    "import random\n",
    "import numpy as np\n",
    "from cs231n.data_utils import load_CIFAR10\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# This is a bit of magic to make matplotlib figures appear inline in the\n",
    "# notebook rather than in a new window.\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n",
    "plt.rcParams['image.interpolation'] = 'nearest'\n",
    "plt.rcParams['image.cmap'] = 'gray'\n",
    "\n",
    "# Some more magic so that the notebook will reload external python modules;\n",
    "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "source": [
    "## CIFAR-10 Data Loading and Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training data shape:  (50000, 32, 32, 3)\n",
      "Training labels shape:  (50000,)\n",
      "Test data shape:  (10000, 32, 32, 3)\n",
      "Test labels shape:  (10000,)\n"
     ]
    }
   ],
   "source": [
    "# Load the raw CIFAR-10 data.\n",
    "cifar10_dir = 'cs231n/datasets/cifar-10-batches-py'\n",
    "\n",
    "# Cleaning up variables to prevent loading data multiple times (which may cause memory issue)\n",
    "try:\n",
    "   del X_train, y_train\n",
    "   del X_test, y_test\n",
    "   print('Clear previously loaded data.')\n",
    "except:\n",
    "   pass\n",
    "\n",
    "X_train, y_train, X_test, y_test = load_CIFAR10(cifar10_dir)\n",
    "\n",
    "# As a sanity check, we print out the size of the training and test data.\n",
    "print('Training data shape: ', X_train.shape)\n",
    "print('Training labels shape: ', y_train.shape)\n",
    "print('Test data shape: ', X_test.shape)\n",
    "print('Test labels shape: ', y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 70 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize some examples from the dataset.\n",
    "# We show a few examples of training images from each class.\n",
    "classes = ['plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']\n",
    "num_classes = len(classes)\n",
    "samples_per_class = 7\n",
    "for y, cls in enumerate(classes):\n",
    "    idxs = np.flatnonzero(y_train == y)\n",
    "    idxs = np.random.choice(idxs, samples_per_class, replace=False)\n",
    "    for i, idx in enumerate(idxs):\n",
    "        plt_idx = i * num_classes + y + 1\n",
    "        plt.subplot(samples_per_class, num_classes, plt_idx)\n",
    "        plt.imshow(X_train[idx].astype('uint8'))\n",
    "        plt.axis('off')\n",
    "        if i == 0:\n",
    "            plt.title(cls)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train data shape:  (49000, 32, 32, 3)\n",
      "Train labels shape:  (49000,)\n",
      "Validation data shape:  (1000, 32, 32, 3)\n",
      "Validation labels shape:  (1000,)\n",
      "Test data shape:  (1000, 32, 32, 3)\n",
      "Test labels shape:  (1000,)\n"
     ]
    }
   ],
   "source": [
    "# Split the data into train, val, and test sets. In addition we will\n",
    "# create a small development set as a subset of the training data;\n",
    "# we can use this for development so our code runs faster.\n",
    "num_training = 49000\n",
    "num_validation = 1000\n",
    "num_test = 1000\n",
    "num_dev = 500\n",
    "\n",
    "# Our validation set will be num_validation points from the original\n",
    "# training set.\n",
    "mask = range(num_training, num_training + num_validation)\n",
    "X_val = X_train[mask]\n",
    "y_val = y_train[mask]\n",
    "\n",
    "# Our training set will be the first num_train points from the original\n",
    "# training set.\n",
    "mask = range(num_training)\n",
    "X_train = X_train[mask]\n",
    "y_train = y_train[mask]\n",
    "\n",
    "# We will also make a development set, which is a small subset of\n",
    "# the training set.\n",
    "mask = np.random.choice(num_training, num_dev, replace=False)\n",
    "X_dev = X_train[mask]\n",
    "y_dev = y_train[mask]\n",
    "\n",
    "# We use the first num_test points of the original test set as our\n",
    "# test set.\n",
    "mask = range(num_test)\n",
    "X_test = X_test[mask]\n",
    "y_test = y_test[mask]\n",
    "\n",
    "print('Train data shape: ', X_train.shape)\n",
    "print('Train labels shape: ', y_train.shape)\n",
    "print('Validation data shape: ', X_val.shape)\n",
    "print('Validation labels shape: ', y_val.shape)\n",
    "print('Test data shape: ', X_test.shape)\n",
    "print('Test labels shape: ', y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training data shape:  (49000, 3072)\n",
      "Validation data shape:  (1000, 3072)\n",
      "Test data shape:  (1000, 3072)\n",
      "dev data shape:  (500, 3072)\n"
     ]
    }
   ],
   "source": [
    "# Preprocessing: reshape the image data into rows\n",
    "X_train = np.reshape(X_train, (X_train.shape[0], -1))\n",
    "X_val = np.reshape(X_val, (X_val.shape[0], -1))\n",
    "X_test = np.reshape(X_test, (X_test.shape[0], -1))\n",
    "X_dev = np.reshape(X_dev, (X_dev.shape[0], -1))\n",
    "\n",
    "# As a sanity check, print out the shapes of the data\n",
    "print('Training data shape: ', X_train.shape)\n",
    "print('Validation data shape: ', X_val.shape)\n",
    "print('Test data shape: ', X_test.shape)\n",
    "print('dev data shape: ', X_dev.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "tags": [
     "pdf-ignore-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(49000, 3073) (1000, 3073) (1000, 3073) (500, 3073)\n"
     ]
    }
   ],
   "source": [
    "# Preprocessing: subtract the mean image\n",
    "# first: compute the image mean based on the training data\n",
    "mean_image = np.mean(X_train, axis=0)\n",
    "# print(mean_image[:10]) # print a few of the elements\n",
    "plt.figure(figsize=(4,4))\n",
    "plt.imshow(mean_image.reshape((32,32,3)).astype('uint8')) # visualize the mean image\n",
    "plt.show()\n",
    "\n",
    "# second: subtract the mean image from train and test data\n",
    "X_train -= mean_image\n",
    "X_val -= mean_image\n",
    "X_test -= mean_image\n",
    "X_dev -= mean_image\n",
    "\n",
    "# third: append the bias dimension of ones (i.e. bias trick) so that our SVM\n",
    "# only has to worry about optimizing a single weight matrix W.\n",
    "X_train = np.hstack([X_train, np.ones((X_train.shape[0], 1))])\n",
    "X_val = np.hstack([X_val, np.ones((X_val.shape[0], 1))])\n",
    "X_test = np.hstack([X_test, np.ones((X_test.shape[0], 1))])\n",
    "X_dev = np.hstack([X_dev, np.ones((X_dev.shape[0], 1))])\n",
    "\n",
    "print(X_train.shape, X_val.shape, X_test.shape, X_dev.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SVM Classifier\n",
    "\n",
    "Your code for this section will all be written inside **cs231n/classifiers/linear_svm.py**. \n",
    "\n",
    "As you can see, we have prefilled the function `compute_loss_naive` which uses for loops to evaluate the multiclass SVM loss function. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss: 9.017608\n",
      "(3073, 10)\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the naive implementation of the loss we provided for you:\n",
    "from cs231n.classifiers.linear_svm import svm_loss_naive\n",
    "import time\n",
    "\n",
    "# generate a random SVM weight matrix of small numbers\n",
    "W = np.random.randn(3073, 10) * 0.0001 \n",
    "\n",
    "loss, grad = svm_loss_naive(W, X_dev, y_dev, 0.000005)\n",
    "print('loss: %f' % (loss, ))\n",
    "print(grad.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `grad` returned from the function above is right now all zero. Derive and implement the gradient for the SVM cost function and implement it inline inside the function `svm_loss_naive`. You will find it helpful to interleave your new code inside the existing function.\n",
    "\n",
    "To check that you have correctly implemented the gradient correctly, you can numerically estimate the gradient of the loss function and compare the numeric estimate to the gradient that you computed. We have provided code that does this for you:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "numerical: 4.024618 analytic: 4.024618, relative error: 5.927206e-11\n",
      "numerical: 26.642546 analytic: 26.642546, relative error: 2.563870e-12\n",
      "numerical: -29.767154 analytic: -29.767154, relative error: 2.152778e-12\n",
      "numerical: -45.305966 analytic: -45.305966, relative error: 2.884926e-13\n",
      "numerical: 13.660630 analytic: 13.660630, relative error: 4.288040e-11\n",
      "numerical: 10.254693 analytic: 10.254693, relative error: 1.857020e-11\n",
      "numerical: 9.506661 analytic: 9.506661, relative error: 1.789061e-11\n",
      "numerical: -35.172007 analytic: -35.172007, relative error: 3.983822e-12\n",
      "numerical: 40.237651 analytic: 40.237651, relative error: 5.164538e-12\n",
      "numerical: -4.410353 analytic: -4.410353, relative error: 5.283877e-11\n",
      "numerical: 2.271964 analytic: 2.271964, relative error: 6.639552e-11\n",
      "numerical: 8.346394 analytic: 8.346394, relative error: 2.391903e-11\n",
      "numerical: 24.747409 analytic: 24.747409, relative error: 1.049933e-11\n",
      "numerical: 1.624586 analytic: 1.624586, relative error: 3.629209e-11\n",
      "numerical: -24.124169 analytic: -24.124169, relative error: 1.093795e-11\n",
      "numerical: 21.849836 analytic: 21.849836, relative error: 2.512934e-12\n",
      "numerical: -2.097001 analytic: -2.097001, relative error: 1.281162e-10\n",
      "numerical: -30.282975 analytic: -30.282975, relative error: 1.439875e-11\n",
      "numerical: -49.887013 analytic: -49.887013, relative error: 3.866772e-12\n",
      "numerical: 2.286411 analytic: 2.286411, relative error: 1.899791e-10\n"
     ]
    }
   ],
   "source": [
    "# Once you've implemented the gradient, recompute it with the code below\n",
    "# and gradient check it with the function we provided for you\n",
    "\n",
    "# Compute the loss and its gradient at W.\n",
    "loss, grad = svm_loss_naive(W, X_dev, y_dev, 0.0)\n",
    "\n",
    "# Numerically compute the gradient along several randomly chosen dimensions, and\n",
    "# compare them with your analytically computed gradient. The numbers should match\n",
    "# almost exactly along all dimensions.\n",
    "from cs231n.gradient_check import grad_check_sparse\n",
    "f = lambda w: svm_loss_naive(w, X_dev, y_dev, 0.0)[0]\n",
    "grad_numerical = grad_check_sparse(f, W, grad)\n",
    "\n",
    "# do the gradient check once again with regularization turned on\n",
    "# you didn't forget the regularization gradient did you?\n",
    "loss, grad = svm_loss_naive(W, X_dev, y_dev, 5e1)\n",
    "f = lambda w: svm_loss_naive(w, X_dev, y_dev, 5e1)[0]\n",
    "grad_numerical = grad_check_sparse(f, W, grad)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-inline"
    ]
   },
   "source": [
    "**Inline Question 1**\n",
    "\n",
    "It is possible that once in a while a dimension in the gradcheck will not match exactly. What could such a discrepancy be caused by? Is it a reason for concern? What is a simple example in one dimension where a gradient check could fail? How would change the margin affect of the frequency of this happening? *Hint: the SVM loss function is not strictly speaking differentiable*\n",
    "\n",
    "$\\color{blue}{\\textit Your Answer:}$ *fill this in.*  \n",
    "max函数在0点处是不可导的以及浮点运算有舍入不能保证绝对精确，不一致可能是这两个原因引起的；只有一个点的不可导对算法影响有限；当L为0时梯度检查会发生失败；要改变这个边界效应可以考虑将SVM损失函数更换为其他处处可导的损失函数。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Naive loss: 9.017608e+00 computed in 0.108457s\n",
      "Vectorized loss: 9.017608e+00 computed in 0.142260s\n",
      "difference: 0.000000\n"
     ]
    }
   ],
   "source": [
    "# Next implement the function svm_loss_vectorized; for now only compute the loss;\n",
    "# we will implement the gradient in a moment.\n",
    "tic = time.time()\n",
    "loss_naive, grad_naive = svm_loss_naive(W, X_dev, y_dev, 0.000005)\n",
    "toc = time.time()\n",
    "print('Naive loss: %e computed in %fs' % (loss_naive, toc - tic))\n",
    "\n",
    "from cs231n.classifiers.linear_svm import svm_loss_vectorized\n",
    "tic = time.time()\n",
    "loss_vectorized, _ = svm_loss_vectorized(W, X_dev, y_dev, 0.000005)\n",
    "toc = time.time()\n",
    "print('Vectorized loss: %e computed in %fs' % (loss_vectorized, toc - tic))\n",
    "\n",
    "# The losses should match but your vectorized implementation should be much faster.\n",
    "print('difference: %f' % (loss_naive - loss_vectorized))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Naive loss and gradient: computed in 0.105716s\n",
      "Vectorized loss and gradient: computed in 0.003183s\n",
      "difference: 0.000000\n"
     ]
    }
   ],
   "source": [
    "# Complete the implementation of svm_loss_vectorized, and compute the gradient\n",
    "# of the loss function in a vectorized way.\n",
    "\n",
    "# The naive implementation and the vectorized implementation should match, but\n",
    "# the vectorized version should still be much faster.\n",
    "tic = time.time()\n",
    "_, grad_naive = svm_loss_naive(W, X_dev, y_dev, 0.000005)\n",
    "toc = time.time()\n",
    "print('Naive loss and gradient: computed in %fs' % (toc - tic))\n",
    "\n",
    "tic = time.time()\n",
    "_, grad_vectorized = svm_loss_vectorized(W, X_dev, y_dev, 0.000005)\n",
    "toc = time.time()\n",
    "print('Vectorized loss and gradient: computed in %fs' % (toc - tic))\n",
    "\n",
    "# The loss is a single number, so it is easy to compare the values computed\n",
    "# by the two implementations. The gradient on the other hand is a matrix, so\n",
    "# we use the Frobenius norm to compare them.\n",
    "difference = np.linalg.norm(grad_naive - grad_vectorized, ord='fro')\n",
    "print('difference: %f' % difference)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Stochastic Gradient Descent\n",
    "\n",
    "We now have vectorized and efficient expressions for the loss, the gradient and our gradient matches the numerical gradient. We are therefore ready to do SGD to minimize the loss."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iteration 0 / 1500: loss 777.288590\n",
      "iteration 100 / 1500: loss 290.429820\n",
      "iteration 200 / 1500: loss 108.074264\n",
      "iteration 300 / 1500: loss 53.355907\n",
      "iteration 400 / 1500: loss 20.190508\n",
      "iteration 500 / 1500: loss 11.044544\n",
      "iteration 600 / 1500: loss 9.908798\n",
      "iteration 700 / 1500: loss 9.106344\n",
      "iteration 800 / 1500: loss 6.556400\n",
      "iteration 900 / 1500: loss 4.599424\n",
      "iteration 1000 / 1500: loss 6.271349\n",
      "iteration 1100 / 1500: loss 12.164805\n",
      "iteration 1200 / 1500: loss 8.363650\n",
      "iteration 1300 / 1500: loss 6.566742\n",
      "iteration 1400 / 1500: loss 7.611296\n",
      "That took 1.700449s\n"
     ]
    }
   ],
   "source": [
    "# In the file linear_classifier.py, implement SGD in the function\n",
    "# LinearClassifier.train() and then run it with the code below.\n",
    "from cs231n.classifiers import LinearSVM\n",
    "svm = LinearSVM()\n",
    "tic = time.time()\n",
    "loss_hist = svm.train(X_train, y_train, learning_rate=1e-7, reg=2.5e4,\n",
    "                      num_iters=1500, verbose=True)\n",
    "toc = time.time()\n",
    "print('That took %fs' % (toc - tic))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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x7n6zuy86wPf8HnCDmZUTJZn7Qv19wIBQfwNw0wG+T6e7+dJjAHhhkW5LFZGDU3uGzp5yoG/i7q8Ar4TycuCkNtrsAj57oO+VSr0L8wGY/aHGQBKRg9P+TMcpwOpNuq4gIgcfJYX99OHGdj/ULSLSZbTnQnMPM8sJ5SPCqKn5qQ8tMz08+RQANmvSHRE5CLXnTOE1oNDMBgMvAF8E/pjKoDJZ8x1I33x4HttrG/bRWkSka2lPUjB33wFcDvzG3T8LHJPasDJX36LdJ0n/N3NljJGIiHS+diUFMzsV+ALwTKjL2jkp83Jz+OXnTwCgf4+CeIMREelk7UkK3wKmAE+6+3tmNpLoAbSsddqoaEimuoamfbQUEela2jP20avufqm73xYuOG9092+kIbaM1aNb9HjHD/+yMOZIREQ6V3vuPnrQzHqbWQ9gIbDIzG5MfWiZq3vB7t6zSffPjjESEZHO1Z7uo6PdfSvRvAd/A0YQ3YGUtZKneXh1SXpHahURSaX2JIX88FzCZcA0d68HMmqgujjcddWJcYcgItLp2pMU7gFWAD2A18zscGBrKoPqCk4c2jfuEEREOl17LjTf5e6D3f1ij6wEzklDbBltaP/ufH38aEB3IYnIwaM9F5r7mNkvmmc7M7Pbic4asl5J70IAqmpqY45ERKRztKf76H5gG/C58NoK/CGVQXUVg/sVAVCheZtF5CCxz/kUgFHu/pmk5VvMbF6K4ulSDu/fHYCVVTv45PD+MUcjInLg2nOmsNPMzmheMLPTAX01Bob0i5LCf/z53ZgjERHpHO1JCl8Ffm1mK8xsBfAr4Np9bWRmhWY228zeNbP3zOyWUD/CzGaZWbmZPWJmBaG+W1guD+uH7/+vlR4FebsP3+NzKmKMRESkc7Tn7qN33f144DjgOHc/ERjfjn3XAuPDticAF5rZKcBtwB3uPhrYDFwT2l8DbA71d4R2XcZ3dLYgIgeBds+85u5bw5PNADe0o727+/awmB9eTpRQHgv1U4keigOYGJYJ68+15EeHM9TPP3t8ouye9c/0iUgXt7/Tcbbrj7WZ5YaL0huA6cAyYIu7N89OUwEMDuXBwGqAsL4aGNDGPic33x5bWRn/EBOfGTc4Ua6pa4wxEhGRA7e/SaFdX4ndvdHdTwCGACcBY/fz/ZL3ea+7l7p7aXFx8YHu7oCZGb/4XHS2sKpqR8zRiIgcmL0mBTPbZmZb23htAw7ryJu4+xaiORhOBfqaWfOtsEOANaG8Bhga3jsP6ANUdei3iUnz7ai/mP5BzJGIiByYvSYFd+/l7r3bePVy930+32BmxWbWN5SLgAnAYqLkcEVoNgl4KpSnhWXC+pe8i3TSD+4bPcT24uINMUciInJg9rf7qD0GAS+b2XzgbWC6uz8NfA+4wczKia4Z3Bfa3wcMCPU3ADelMLZOlZNjfOLwfuRk/GVxEZGP154nmveLu88H9hhf2t2XE11faF2/C/hsquJJtTGH9GTOys08MbeCy8cNiTscEZH9ksozhazS3IV0w6N6XkFEui4lhU5y3TmjE+XaBt2aKiJdk5JCJ8nNMX5y2T8A8Nd318YcjYjI/lFS6ETFvboBGiBPRLouJYVO9KkxA+MOQUTkgCgpdKLuBXl889wxAMyv2BJvMCIi+0FJoZMdeWgvAC791RvsqGvYR2sRkcyipNDJRhX3TJTvfHFpjJGIiHSckkInO/LQXgzsGV1wvue15TFHIyLSMUoKKfDw5JPjDkFEZL8oKaTAyIG7u5C6yJh+IiKAkkJK5OQYXz59OAB36LqCiHQhSgop0tgUnSHcNUNJQUS6DiWFFPny6SMS5bIVm2KMRESk/ZQUUmTEwB5ce+ZIAK747Vts3VUfc0QiIvumpJBCR5T0SpSXbdgeYyQiIu2TsqRgZkPN7GUzW2Rm75nZN0N9fzObbmZLw89+od7M7C4zKzez+WY2LlWxpcvJI/snyr//+4cxRiIi0j6pPFNoAL7j7kcDpwDXm9nRRNNsznD3McAMdk+7eREwJrwmA3enMLa0GNKvO49eeyoAm7bXxRyNiMi+pSwpuPtad58bytuAxcBgYCIwNTSbClwWyhOBP3lkJtDXzAalKr50OWlEfwb0KOCt5VV6ZkFEMl5arimY2XCi+ZpnASXu3jwLzTqgJJQHA6uTNqsIda33NdnMysysrLKyMnVBd6KrThoGwCsfdI14RSR7pTwpmFlP4HHgW+6+NXmdR1+dO/T12d3vdfdSdy8tLi7uxEhT5/OfHArAl//4Nptr1I0kIpkrpUnBzPKJEsID7v5EqF7f3C0Ufm4I9WuAoUmbDwl1Xd7gvkWJ8sV3vR5jJCIiHy+Vdx8ZcB+w2N1/kbRqGjAplCcBTyXVXx3uQjoFqE7qZurScnKM1248B4C11bvYVd8Yc0QiIm1L5ZnC6cAXgfFmNi+8LgZuBSaY2VLgvLAM8CywHCgHfgdcl8LY0q55/maAB2etijESEZG9s658R0xpaamXlZXFHUa7TX1zBT+a9h4Ar3/3HIb27x5zRCKSjcxsjruXtrVOTzSn0aTThifKf3hjRWxxiIjsjZJCmn3/4rEA3P+GnnAWkcyjpJBm/5o0eurrS/XcgohkFiWFNMvLzSHHovIX75vN2uqduhtJRDKGkkIM/nL96YnyqT99ia89+E6M0YiI7KakEIPjhvTlpBG7R1B9cfH6GKMREdlNSSEm9/zLJxLl44f2jS8QEZEkSgox6dejgOX/fTGfHN6Pd1dv4aX3dbYgIvFTUohRTo7x0ZZdgJ5bEJHMoKQQszs+fwIAry/dyN2vLIs3GBHJekoKMUu+4Hzbc+9T19AUYzQiku2UFDLAK/9xdqJ8xA//RkOjEoOIxENJIQMMH9iDGd85K7H8P89/QGNT1x2oUES6LiWFDDGquGeifO9ry7nnNV1fEJH0U1LIIK/eeHaiPH91dXyBiEjWUlLIIIcP6MG1Z44E4Ln31lG9oz7miEQk26RyOs77zWyDmS1MqutvZtPNbGn42S/Um5ndZWblZjbfzMalKq5MN+XioxJzOl9+9xtsrqmLOSIRySapPFP4I3Bhq7qbgBnuPgaYEZYBLgLGhNdk4O4UxpXxnvnGGQAsq6zhxB9PZ3nl9pgjEpFskbKk4O6vAZtaVU8EpobyVOCypPo/eWQm0NfMBqUqtkzXt3sBTyWNpDr+9lep2l4bY0Qiki3SfU2hxN3XhvI6oCSUBwOrk9pVhLo9mNlkMyszs7LKyoN3kprjh/Zl9vfPTSx/4icvxhiNiGSL2C40u7sDHb4Z393vdfdSdy8tLi5OQWSZ45DehS2Wb3/hg8QTz0++U8H767bGEZaIHMTSnRTWN3cLhZ8bQv0aYGhSuyGhLus9MvkUenXLA+B/XyrniB/+jdeXVvLtR97lwl++HnN0InKwSXdSmAZMCuVJwFNJ9VeHu5BOAaqTupmy2skjB7Dglgv4j/OPSNR98b7ZibKefBaRzpTKW1IfAt4CjjSzCjO7BrgVmGBmS4HzwjLAs8ByoBz4HXBdquLqqq4/ZzQFuXv+c905Y2kM0YjIwcqirv2uqbS01MvKyuIOI23qG5v49/+bu8f0nT+9/FiuOmlYTFGJSFdjZnPcvbStdXqiuQvJz83h95NKufPKE1rUT3liAR9t2cnCNdXUNjTGE5yIHBTy4g5AOm7iCYM5alBvzr/jtUTdabe+lCi/d8sF9Oimf1oR6TidKXRRR5T04pZLj2lz3Y2PvZvmaETkYKGk0IVNOm04y//74j3qn12wjvE/f4XhNz3D2uqdMUQmIl2VkkIXl5NjrLj1Ei4f1/IB8OUbawA49acvtbWZiEiblBQOEr/43Am8NWV8m+umPDGf3722nH/5/aw0RyUiXY2uRh5EBvUp4q0p4znv9lepqdt9F9JDs3cPKzVzeRXjhvWjIE/fB0RkT/rLcJAZ1KeIN286l9duPKfN9VfeO5Mv/3E2D85aBUBdQxMrq2rSGaKIZDCdKRyE+nTPp0/3fF749pm8Wb6Rm/+6qMX6N8qreKO8iuJe3fjNK+W8s2oL7/7X+fTpnh9TxCKSKXSmcBA7oqQXXzp9BO//+EJmJQ3D3ezf/lTGO6u2ALBkw7bE9J9L1m/jrJ+9zMOzV9HU5Cxdv42q7bXc9Ph8doZuqaXrt7GuelfafhcRSQ+dKWSBwvxcCvNzmfGds3hu4TrufW051Ttbzv/82d++BcAZowfy9/KNANz0xAK21zbwk2cWc/yQPrxbUc3xQ/ty1UnDmBAenFtx6yXp/WVEJKWUFLLIqOKeXH/OaL561ihyDD7cWMOdM5by1LyPEm2aE0KznzyzGIDlldF1h7qGJp5/b12b+399aSVHlvTaYx4IEek6NCCesGbLTh6ctZKpb65ke21Dh7Z9+wfnMbBnARWbd/Kp/3kZgAe+cjJ5Ocbn753Jc9/6FGMP7Z2KsKWLe3bBWk4fNVDXsmLwcQPiKSnIHn7/+nLunLGUbbs6liCS/fPJwxJ3ON3+2ePZsK2Wr541EjNLtNlV38jOukb69ShI1G3Ytovbn1/CLROPoTA/lx11DfzvS+V8Y/wYigpy9/+XykKrqnbQqzCvxfGN28sfbODLf3ibqf96EpPun834sYdw/5c+GXdYWefjkoK6j2QPX/nUSK45YwQ76xvZVFPHKx9Ucsmxg2h0p7Sdc0U3JwSA7/w5Govp4bdXMfnMkVx4zKFs3lHPeb94NdHm+nNGMXxAD258bD4Aj5St5tbLj2Xd1l3c/coyint24zPjhtCzMI/cHMPd2bKjnv83cyX/esYIAArzclhRVcO2XQ2cOKwfALM/3MSRJb3oXZRHk0NuTpSU/vMvCznziGLKVm7inleXc+7YQ7jsxMFc+A+H8tGWnRw+oEeL38fdW2y/v55dsJbKbbVMOm34PtvOWbmZovxc/l5eyeQzR+2z/a76Rgrzc9lV30hDk3Pmz16mpHc3Zn3/vD3abttVz1/fXctVJw1tkag7wt33uu2rSyq54ZF5TPv6GdTUNjC4bxF/mbeGN8urAHh9STS/esXmHS22e27hWnp2y+eMMQM7FEtDYxP1jU5RQS5V22tZvXknJwzt265tX11SyWmjBpDfxnwle1O2YhO5OZb4fwbR0PYd2Udr1TvqWfhRNaeP7tjv3tl0piAdsqpqB9MXr+eh2as4tHfhHtcgMskT153G5b95s0Xd4/9+Gh9t2cnXH3qnzW3OPKKY15ZU8j9XHMf66l3825kjWbx2K3+eU8GDs1ax4tZLaGpyfvbCB9z9yjIuOXYQP/r00dQ2NPG3hWt5felGvnXeGHbVN1G2YjMTTziM4QN3J5jhNz0DwI0XHMkL763jV/88jrKVm1izeSfVO+v59oQjKMrPZVllTYuk+f6PL2Tx2q1Ubqvl/GMO5fP3vMWsDzcx9z8nMH3ROv7zL+9R19jEy/9xNj9//gOeWbB74sJ3/nMCM5dXUVPXyD+dOJiVVTX88sWlTHv3Ix6efAonDuvL1x58h6+eNZIXF2/giJKeLNtQw6ePP4zyDdtZ+FE137twLOUbtvPBum2cecRAGpucib9+g5VVO7jtM8fyvccXAPC7q0v57avLmLNyc4vj+onD++1R12zef02gW14u9U1NHHfzCwC8edN45qzczISjSyjIzaG+qYn8nBxyWiXlz/32LUYM7MGmHXVMX7SeR689lc/d81bi33r6ovXcdNFYAMo3bE8c0y+ecjjHDumDu/O9xxcwtH8Rf/jSSTw8exW9i/J5c9lGLj9xCJ8+/jCKCnJZvHYra6t3Mn5sCSs21nD2z18BYOyhvfjZFccz4/31/PLFpZw2agBlKzez6JYLAGh0p1ve7jPcBRXVDOvfnT/PWc3767Zx22eOI8fAHUZ+/1kA/v69cyjIy6G+0TmsTyHffmQelxx3GCeP7M9Pn13MZScM5uSRA9o8lu3VZbqPzOxC4E4gF/i9u9/6ce2VFDJDfWMTtQ1N5Bi8tqSS1Zt2MuP99fzbp0ays76Rrz3Y9h/gbHH0oN70LMxjeeV2Nm6va9c2p40awJvLqtpc17swj60H0LXX1V171kg+2rKLw/oUcs9ry1P+fh/3b7E3Xx8/mv99qRyA70w4gpkfVrGuehfLKvd8UHT4gO6sqNqxRz3AuGF9mRtuG0925SeHcvOlURfr/ugSScHMcoElwASgAngbuMrdF+1tGyWFrmV7bQPbdtVTvmE7Pbrl8cjs1XzjvDH8uWw1ry6pZOn67eysb+SoQb245NjDuO259wEY0q+Iis0a7VUk2XcvPJLrzh69X9t2laRwKnCzu18QlqcAuPtP97aNksLBrXpHPb2L8jAzGhqbWLd1FwN7dmNHXSPdC3KpqW2gd1E+j5atpjAvl6qaWsaPPYRh/XuwZWcdTU2Qn2s8s2AtU99cwaPXnso7q7bw5DtrMIOn56+lV7c8plx8FL97fTnfOf8I3l+7jW+cO4avPzSX599bv9fYJp5wGEeU9OLXL5dzxSeG8H8zV5JjRkNT25+nkt7dWL+19mN/3+4Fueyoa9/MecnPkwAcPqA7K/fybbN/jwKmXDQWB74brtkADOhRQFVNyzOXgT0L9jib6VWYx7ZdDfQpyqd6Zz2jinu0+Y13wtElTF+05zF77cZzuPf1Zfx96Ubyc3NYumH7x/5ulxw3iJcWb+C0UQOY9eGmdt8R97MrjuPp+Wt5dUklIwf2SIwUnKxbXg6XjxvM6aMHMuXxBWyrbeC8ow7hxcUbgJY3SLTW1r/h/151YouuyF987nhuePTj5zMZ3LeINVs69iVn7KG9eH/dtsTyl04bzg8uOWq/r2F0laRwBXChu38lLH8RONndv9aq3WRgMsCwYcM+sXLlyrTHKtllZ13jPu98amhsIq8dH9Cq7bX0616wR994a9U76mlyZ1dDdIfWssoazjvqEIDExV13Z/qi9Zwz9pAWfxwam5z6xibuf+NDThren9Lh/RPrPtqyk+Je3Vq0b2hsYvOOeop7daOhsYmGJicvx8gx22uc7s7itds4+rDeiQvcyTZur8WAAT277bHtlh11vFFexcXHHtqivqHJP/aPXPWOemobG+ldmN/i/RauqeaYw3onjktdQ1NiwMfGJufht1cxurhnm/3wVdtr94ixemc99Y1NFOXn4sC8VVtoaGri7CMPoWLzDgpyc9hZ30jfooLE7bQ76xpZt3UXI5KuH7k7j89dwwlD+5CXk0NJ78LE/6PqnfX0KYq2XVu9k57d8uhVGC3XNzaxMtw51qcon255OS1+t/xc2++bA5odVEkhmc4UREQ67uOSQiaNfbQGGJq0PCTUiYhImmRSUngbGGNmI8ysALgSmBZzTCIiWSVjHl5z9wYz+xrwPNEtqfe7+3sxhyUiklUyJikAuPuzwLNxxyEikq0yqftIRERipqQgIiIJSgoiIpKgpCAiIgkZ8/Da/jCzSmB/H2keCGTuEJ8RxXjgMj0+yPwYMz0+UIwddbi7F7e1oksnhQNhZmV7e6IvUyjGA5fp8UHmx5jp8YFi7EzqPhIRkQQlBRERScjmpHBv3AG0g2I8cJkeH2R+jJkeHyjGTpO11xRERGRP2XymICIirSgpiIhIQlYmBTO70Mw+MLNyM7spphiGmtnLZrbIzN4zs2+G+v5mNt3Mloaf/UK9mdldIeb5ZjYujbHmmtk7ZvZ0WB5hZrNCLI+Eoc4xs25huTysH56G2Pqa2WNm9r6ZLTazUzPtGJrZt8O/8UIze8jMCuM+hmZ2v5ltMLOFSXUdPm5mNim0X2pmk1Ic38/Cv/N8M3vSzPomrZsS4vvAzC5Iqk/ZZ72tGJPWfcfM3MwGhuW0H8P95u5Z9SIalnsZMBIoAN4Fjo4hjkHAuFDuBSwBjgb+B7gp1N8E3BbKFwN/Aww4BZiVxlhvAB4Eng7LjwJXhvJvgX8P5euA34bylcAjaYhtKvCVUC4A+mbSMQQGAx8CRUnH7ktxH0PgTGAcsDCprkPHDegPLA8/+4VyvxTGdz6QF8q3JcV3dPgcdwNGhM93bqo/623FGOqHEk0BsBIYGNcx3O/fK843j+UXhlOB55OWpwBTMiCup4AJwAfAoFA3CPgglO8Brkpqn2iX4riGADOA8cDT4T/1xqQPZ+J4hg/CqaGcF9pZCmPrE/7gWqv6jDmGRElhdfjQ54VjeEEmHENgeKs/uh06bsBVwD1J9S3adXZ8rdb9E/BAKLf4DDcfw3R81tuKEXgMOB5Ywe6kEMsx3J9XNnYfNX9Im1WEutiELoITgVlAibuvDavWASWhHFfcvwS+CzSF5QHAFndvaCOORIxhfXVonyojgErgD6F76/dm1oMMOobuvgb4ObAKWEt0TOaQOccwWUePW5yfpX8l+ubNx8SR9vjMbCKwxt3fbbUqY2Lcl2xMChnFzHoCjwPfcvetyes8+uoQ2z3DZvaPwAZ3nxNXDPuQR3T6fre7nwjUEHV7JGTAMewHTCRKYIcBPYAL44qnveI+bh/HzH4ANAAPxB1LMjPrDnwf+K+4YzkQ2ZgU1hD1+TUbEurSzszyiRLCA+7+RKheb2aDwvpBwIZQH0fcpwOXmtkK4GGiLqQ7gb5m1jxrX3IciRjD+j5AVQrjqwAq3H1WWH6MKElk0jE8D/jQ3SvdvR54gui4ZsoxTNbR45b242lmXwL+EfhCSFyZFN8oouT/bvjMDAHmmtmhGRTjPmVjUngbGBPu/iggupg3Ld1BmJkB9wGL3f0XSaumAc13IEwiutbQXH91uIvhFKA66VQ/Jdx9irsPcffhRMfpJXf/AvAycMVeYmyO/YrQPmXfNt19HbDazI4MVecCi8igY0jUbXSKmXUP/+bNMWbEMWylo8fteeB8M+sXzojOD3UpYWYXEnVlXuruO1rFfWW4c2sEMAaYTZo/6+6+wN0Pcffh4TNTQXQzyToy5Bi2S5wXNOJ6Ed0JsITozoQfxBTDGUSn5/OBeeF1MVH/8QxgKfAi0D+0N+DXIeYFQGma4z2b3XcfjST60JUDfwa6hfrCsFwe1o9MQ1wnAGXhOP6F6A6OjDqGwC3A+8BC4P8R3SUT6zEEHiK6xlFP9Mfrmv05bkR9++Xh9eUUx1dO1P/e/Hn5bVL7H4T4PgAuSqpP2We9rRhbrV/B7gvNaT+G+/vSMBciIpKQjd1HIiKyF0oKIiKSoKQgIiIJSgoiIpKgpCAiIglKCtLlmNn28HO4mf1zJ+/7+62W3+zM/Xc2M/uSmf0q7jjk4KGkIF3ZcKBDSSHpKeK9aZEU3P20DsbUpZhZbtwxSGZRUpCu7FbgU2Y2z6I5C3LDmPtvhzHrrwUws7PN7HUzm0b0NDFm9hczm2PRPAeTQ92tQFHY3wOhrvmsxMK+F5rZAjP7fNK+X7Hdczo8EJ5cbiG0uc3MZpvZEjP7VKhv8U3fzJ42s7Ob3zu853tm9qKZnRT2s9zMLk3a/dBQv9TMfpS0r38J7zfPzO5pTgBhv7eb2btEI4mK7Bb303N66dXRF7A9/Dyb8JR1WJ4M/DCUuxE96TwitKsBRiS1bX5at4joSeMByftu470+A0wnGqO/hGj4ikFh39VEY9bkAG8BZ7QR8yvA7aF8MfBiKH8J+FVSu6eBs0PZCU/nAk8CLwD5RMMyz0vafi3R08jNv0spcBTwVyA/tPsNcHXSfj8X97+jXpn52teptEhXcj5wnJk1jynUh2gcnDpgtrt/mNT2G2b2T6E8NLT7uIHnzgAecvdGooHjXgU+CWwN+64AMLN5RN1af29jH82DHs4JbfalDngulBcAte5eb2YLWm0/3d2rwvs/EWJtAD4BvB1OXIrYPcBdI9FAjCJ7UFKQg4kBX3f3FgOKhe6YmlbL5xFNZrPDzF4hGnNof9UmlRvZ++eqto02DbTsxk2Oo97dm8ehaWre3t2bWl0baT1WjRMdi6nuPqWNOHaF5CayB11TkK5sG9FUps2eB/7doiHJMbMjLJp0p7U+wOaQEMYSTY/YrL55+1ZeBz4frlsUE03FOLsTfocVwAlmlmNmQ4GT9mMfEyyaX7kIuAx4g2hguyvM7BBIzL98eCfEKwc5nSlIVzYfaAwXTP9INNfDcKIx7I1oVrbL2tjuOeCrZraYaFTNmUnr7gXmm9lcj4YJb/Yk0UXZd4m+iX/X3deFpHIg3iCaUnQRsBiYux/7mE3UHTQE+D93LwMwsx8CL5hZDtFIntcTzRssslcaJVVERBLUfSQiIglKCiIikqCkICIiCUoKIiKSoKQgIiIJSgoiIpKgpCAiIgn/H/KjfCIJof2NAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# A useful debugging strategy is to plot the loss as a function of\n",
    "# iteration number:\n",
    "plt.plot(loss_hist)\n",
    "plt.xlabel('Iteration number')\n",
    "plt.ylabel('Loss value')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training accuracy: 0.100265\n",
      "validation accuracy: 0.087000\n"
     ]
    }
   ],
   "source": [
    "# Write the LinearSVM.predict function and evaluate the performance on both the\n",
    "# training and validation set\n",
    "y_train_pred = svm.predict(X_train)\n",
    "print('training accuracy: %f' % (np.mean(y_train == y_train_pred), ))\n",
    "y_val_pred = svm.predict(X_val)\n",
    "print('validation accuracy: %f' % (np.mean(y_val == y_val_pred), ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "tags": [
     "code"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.313\n",
      "0.289\n",
      "0.064\n",
      "0.087\n",
      "lr 1.000000e-07 reg 2.500000e+04 train accuracy: 0.283388 val accuracy: 0.313000\n",
      "lr 1.000000e-07 reg 5.000000e+04 train accuracy: 0.275347 val accuracy: 0.289000\n",
      "lr 5.000000e-05 reg 2.500000e+04 train accuracy: 0.064163 val accuracy: 0.064000\n",
      "lr 5.000000e-05 reg 5.000000e+04 train accuracy: 0.100265 val accuracy: 0.087000\n",
      "best validation accuracy achieved during cross-validation: 0.313000\n"
     ]
    }
   ],
   "source": [
    "# Use the validation set to tune hyperparameters (regularization strength and\n",
    "# learning rate). You should experiment with different ranges for the learning\n",
    "# rates and regularization strengths; if you are careful you should be able to\n",
    "# get a classification accuracy of about 0.39 on the validation set.\n",
    "\n",
    "#Note: you may see runtime/overflow warnings during hyper-parameter search. \n",
    "# This may be caused by extreme values, and is not a bug.\n",
    "\n",
    "learning_rates = [1e-7, 5e-5]\n",
    "regularization_strengths = [2.5e4, 5e4]\n",
    "\n",
    "# results is dictionary mapping tuples of the form\n",
    "# (learning_rate, regularization_strength) to tuples of the form\n",
    "# (training_accuracy, validation_accuracy). The accuracy is simply the fraction\n",
    "# of data points that are correctly classified.\n",
    "results = {}\n",
    "best_val = -1   # The highest validation accuracy that we have seen so far.\n",
    "best_svm = None # The LinearSVM object that achieved the highest validation rate.\n",
    "\n",
    "################################################################################\n",
    "# TODO:                                                                        #\n",
    "# Write code that chooses the best hyperparameters by tuning on the validation #\n",
    "# set. For each combination of hyperparameters, train a linear SVM on the      #\n",
    "# training set, compute its accuracy on the training and validation sets, and  #\n",
    "# store these numbers in the results dictionary. In addition, store the best   #\n",
    "# validation accuracy in best_val and the LinearSVM object that achieves this  #\n",
    "# accuracy in best_svm.                                                        #\n",
    "#                                                                              #\n",
    "# Hint: You should use a small value for num_iters as you develop your         #\n",
    "# validation code so that the SVMs don't take much time to train; once you are #\n",
    "# confident that your validation code works, you should rerun the validation   #\n",
    "# code with a larger value for num_iters.                                      #\n",
    "################################################################################\n",
    "# *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n",
    "\n",
    "for lr in learning_rates:\n",
    "    for rs in regularization_strengths:\n",
    "        svm = LinearSVM()\n",
    "        loss_hist = svm.train(X_train, y_train, learning_rate=lr, reg=rs,\n",
    "                      num_iters=1500, verbose=False)\n",
    "        y_train_pred = svm.predict(X_train)\n",
    "        y_val_pred = svm.predict(X_val)\n",
    "        \n",
    "        y_train_acc = np.mean(y_train == y_train_pred)\n",
    "        y_val_acc = np.mean(y_val == y_val_pred)\n",
    "        print(y_val_acc)\n",
    "        \n",
    "        results[(lr, rs)] = (y_train_acc, y_val_acc)\n",
    "        if best_val < y_val_acc:\n",
    "            best_val = y_val_acc\n",
    "            best_svm = svm\n",
    "\n",
    "# *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n",
    "    \n",
    "# Print out results.\n",
    "for lr, reg in sorted(results):\n",
    "    train_accuracy, val_accuracy = results[(lr, reg)]\n",
    "    print('lr %e reg %e train accuracy: %f val accuracy: %f' % (\n",
    "                lr, reg, train_accuracy, val_accuracy))\n",
    "    \n",
    "print('best validation accuracy achieved during cross-validation: %f' % best_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "tags": [
     "pdf-ignore-input"
    ]
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the cross-validation results\n",
    "import math\n",
    "x_scatter = [math.log10(x[0]) for x in results]\n",
    "y_scatter = [math.log10(x[1]) for x in results]\n",
    "\n",
    "# plot training accuracy\n",
    "marker_size = 100\n",
    "colors = [results[x][0] for x in results]\n",
    "plt.subplot(2, 1, 1)\n",
    "plt.scatter(x_scatter, y_scatter, marker_size, c=colors, cmap=plt.cm.coolwarm)\n",
    "plt.colorbar()\n",
    "plt.xlabel('log learning rate')\n",
    "plt.ylabel('log regularization strength')\n",
    "plt.title('CIFAR-10 training accuracy')\n",
    "\n",
    "# plot validation accuracy\n",
    "colors = [results[x][1] for x in results] # default size of markers is 20\n",
    "plt.subplot(2, 1, 2)\n",
    "plt.scatter(x_scatter, y_scatter, marker_size, c=colors, cmap=plt.cm.coolwarm)\n",
    "plt.colorbar()\n",
    "plt.xlabel('log learning rate')\n",
    "plt.ylabel('log regularization strength')\n",
    "plt.title('CIFAR-10 validation accuracy')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "linear SVM on raw pixels final test set accuracy: 0.288000\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the best svm on test set\n",
    "y_test_pred = best_svm.predict(X_test)\n",
    "test_accuracy = np.mean(y_test == y_test_pred)\n",
    "print('linear SVM on raw pixels final test set accuracy: %f' % test_accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "tags": [
     "pdf-ignore-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the learned weights for each class.\n",
    "# Depending on your choice of learning rate and regularization strength, these may\n",
    "# or may not be nice to look at.\n",
    "w = best_svm.W[:-1,:] # strip out the bias\n",
    "w = w.reshape(32, 32, 3, 10)\n",
    "w_min, w_max = np.min(w), np.max(w)\n",
    "classes = ['plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']\n",
    "for i in range(10):\n",
    "    plt.subplot(2, 5, i + 1)\n",
    "      \n",
    "    # Rescale the weights to be between 0 and 255\n",
    "    wimg = 255.0 * (w[:, :, :, i].squeeze() - w_min) / (w_max - w_min)\n",
    "    plt.imshow(wimg.astype('uint8'))\n",
    "    plt.axis('off')\n",
    "    plt.title(classes[i])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-inline"
    ]
   },
   "source": [
    "**Inline question 2**\n",
    "\n",
    "Describe what your visualized SVM weights look like, and offer a brief explanation for why they look they way that they do.\n",
    "\n",
    "$\\color{blue}{\\textit Your Answer:}$ *fill this in*  \n",
    "有几个weights看起来像模糊化了的标签，比如car和horse，其他的看不出来和标签的关系。这说明weights中含有标签的部分信息，但信息比较抽象，不是简单可视化就能表现出来的。"
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